code: def dict_to_row(schema, row_dict):
      import pyspark
  
      err_msg = "Dictionary fields \n{}\n do not match schema fields \n{}".format(
          "\n".join(sorted(row_dict.keys())), "\n".join(schema.keys())
      )
      invalidInputError(
          set(row_dict.keys()) == set(schema.keys()), err_msg
      )
  
      row = {}
      for k, v in row_dict.items():
          schema_field = schema[k]
          if schema_field.feature_type == FeatureType.IMAGE:
              image_path = v
              with open(image_path, "rb") as f:
                  img_bytes = f.read()
              row[k] = bytearray(img_bytes)
          elif schema_field.feature_type == FeatureType.NDARRAY:
              with BytesIO() as memfile:
                  np.savez_compressed(memfile, arr=v)
                  row[k] = bytearray(memfile.getvalue())
          else:
              row[k] = v
      return pyspark.Row(**row)
  
imports: from collections import namedtuple
  from io import BytesIO
  import numpy
  from itertools import chain, islice
  from enum import Enum
  import json
  from bigdl.dllib.utils.log4Error import *
  from typing import Any, Dict, Iterator, List, Union, TYPE_CHECKING
label_desc: Use image node_id 7 for calling a global function with example usage: dict_to_row(schema, row_dict) and returns: pyspark